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AI Readiness Audit – Is Your Business Ready for AI?

A report assessing your company's readiness for AI implementation: processes, data, infrastructure, and competencies. Know where to start and what to avoid.

Author: Paweł Wiszniewski · Updated:

Key takeaways

A report assessing your company's readiness for AI implementation: processes, data, infrastructure, and competencies. Know where to start and what to avoid.

  • ›Clear readiness map: you know exactly where you stand on the AI maturity scale and what you need to build before committing budget to specific implementations.
  • ›Ranked automation candidates with estimated ROI
  • ›Risk identification: data gaps, privacy issues (GDPR), competency shortfalls, and infrastructure dependencies

Price: from €1,500

SERVICE DETAILS

An AI readiness audit is a comprehensive assessment of your organisation's readiness to deploy artificial intelligence — before you commit budget to specific tools. I evaluate four layers: processes (which are repetitive and generate sufficient data), data (quality, availability, structuring), IT infrastructure (source systems, APIs, data security), and team competencies. The output is a report with a readiness matrix, a ranked list of processes to automate, and a concrete list of quick wins and long-term initiatives — each with estimated ROI.

› INVESTMENT:

from €1,500
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Paweł Wiszniewski · my approach

What I do differently

I'm an advisor who also implements: I build agents, RAG systems and automations, so in the audit I assess not just strategy but whether your data and systems can actually deliver what vendors promise. The most common reason AI projects fail isn't the model but messy data, which is why a large part of the audit focuses on it. I don't sell any tool or take vendor commissions, so I can tell you plainly when AI isn't a good idea for you.

Read my guide on the blog

My take and a real-world example

My take

In my view the best AI readiness audit is one that sometimes advises against implementation. If data is messy, spending on a model is like buying a sports car with no road to drive on.

From the market

Gartner predicted that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, citing poor data quality, inadequate risk controls and unclear business value. Those are exactly the areas I check in the audit before any budget decision.

Sources: Gartner: 30% of GenAI projects abandoned after PoC (2024)

Who this is for

  • Companies that know they “should do something with AI” but don't know where to start.
  • Boards facing a decision on an AI budget.
  • Companies that started several AI pilots and see no results.
  • Organizations that want to check risks: data, GDPR, the AI Act and security.

What you get

  • Interviews with key people and a review of processes, data and systems.
  • A maturity assessment: data, infrastructure, skills and culture.
  • A list of AI use cases with estimated impact and implementation difficulty.
  • A legal and security risk map with recommendations.
  • A report with first steps and a presentation for the board.

How results are measured

  • Number of AI use cases with a justified business return.
  • Data readiness for the use cases in the report.
  • Board decisions made on the basis of the report.
  • Time from audit to first implementation.

Key Benefits

Clear readiness map: you know exactly where you stand on the AI maturity scale and what you need to build before committing budget to specific implementations.

Ranked automation candidates with estimated ROI — you prioritise AI initiatives by savings potential, not by the IT department's enthusiasm.

Risk identification: data gaps, privacy issues (GDPR), competency shortfalls, and infrastructure dependencies — all surfaced before the project starts.

A list of concrete quick wins deployable within 30-90 days — early visible results that build board confidence for further AI investment.

Independent opinion with no conflict of interest — I do not recommend tools I am a partner for. You receive an objective assessment, not a sales pitch.

The Process

1

Kick-off and process mapping

Workshops with key stakeholders (1-2 days) or a series of interviews. I map operational processes, identify data owners, and gather information about current IT systems.

2

Data and infrastructure analysis

I assess data quality and availability, IT security and integrations, and GDPR compliance. I identify bottlenecks and gaps that would block AI deployments.

3

Competency and culture assessment

Surveys and interviews with employees evaluate cultural readiness for AI and competency gaps. I identify AI champions in the organisation and potential points of resistance.

4

Report and recommendations

I deliver a comprehensive report with a readiness matrix, ranked AI initiatives (quick wins + long-term), estimated ROI, and an implementation roadmap with timeline and budget.

Common mistakes

  • Starting with a tool choice instead of the process and business problem.
  • Pilots without a defined success metric that never end.
  • Ignoring data quality and availability when assessing readiness.
  • No decision on who owns AI in the company or an AI usage policy.

When this service is not worth it

  • You already have a specific, documented AI project and only need someone to build it.
  • The company is mid-reorganization and processes change week to week.
  • You want confirmation of a decision already made, not an independent assessment.

Frequently Asked Questions

How long does an AI readiness audit take?

A standard audit takes 2-4 weeks, depending on organisation size and stakeholder availability. For companies under 50 people — 2 weeks. For corporations with multiple divisions — up to 6 weeks.

How does an audit differ from an initial consultation?

A consultation (1-2 hours) is a conversation about AI possibilities and a preliminary assessment. An AI readiness audit is a deep analysis delivering a formal report with a matrix, rankings, and a concrete action plan. The audit is an investment, not a cost — the report stays with the company for years.

What if the audit shows we are not ready for AI?

That is the most valuable possible outcome — before you spend €50,000 on an implementation that will not deliver results. In that case I provide a preparation roadmap: what to build over the next 6-12 months so that an AI deployment has a real chance of success.

Do you help with implementation after the audit?

Yes, if that is what you want. The audit is independent and you can implement the recommendations with any partner. If you choose to continue with me, the audit forms the basis for the implementation strategy.

What do I need to prepare for an AI readiness audit?

Access to a few people key to your processes (usually for short interviews), a description or diagram of your systems and where your data is stored. I prepare the rest from the interviews.

Does the audit cover GDPR and the EU AI Act?

Yes. I check what data would go into AI systems, whether you have a legal basis and vendor agreements, and which use cases may fall under specific AI Act obligations. It doesn't replace legal advice but shows where it's needed.

How much does an AI readiness audit cost?

The audit starts from €1,500. The price depends on company size, the number of departments covered and system complexity.

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From the blog

AI & Automation

Where to Start with AI Implementation in Your Company

AI implementation starts not with choosing a tool, but with identifying one repetitive process that wastes the most human time. Learn step by step how to select, map, and automate that process.

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Data Readiness - How to Prepare Your Company's Data for AI Before You Spend a Dollar on Implementation

Gartner projects that by the end of 2026, organizations will abandon 60% of AI projects specifically because they lacked AI-ready data - not because the model was bad. MIT NANDA's July 2025 report went further: with $30-40 billion invested by companies in generative AI, 95% of pilots delivered no measurable return. The common denominator in both cases isn't technological - it's the data mess nobody cleaned up before signing the vendor contract. Here's how to audit your data sources, assess their quality, and clean them up BEFORE the rollout, not while putting out fires afterward.

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AI Automation ROI - How to Calculate Returns Before You Sign Anything

Most companies deploy AI because they feel they have to, then measure success by whether the system runs at all. I show you how to calculate ROI before deployment - four numbers, one calculator, three case studies with real figures.

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BIAŁYSTOK, PL
+48 732 022 086pawel.wiszniewski95@gmail.com